Brain as Prediction Machine

So I’m really interested right now in how the brain operates as a prediction machine. Basically, one of our core brain functions seems to be guessing what is going to happen next.

I think this has some really fascinating implications for behavior change.  Humans are (in many ways) bad at risk prediction.  More people seem to be afraid of flying than driving, despite data that shows the riskiest part of any flight is the drive to the airport. We are often more afraid of things that are scary than things that are likely — sedentary behavior is far more likely than bungee jumping to injure us, but we probably wouldn’t rate sitting on the couch as more risky than jumping off a bridge attaching to a giant rubber band.

Classic behaviors that are difficult to change include things like diet, exercise, smoking, texting while driving.  In workplace contexts, I might look at safety procedures or sanitary food handling.  All of these activities involve some assessment of the risk involved and some prediction of outcomes, either consciously or unconsciously.

Here are some interesting things I’ve been looking at regarding this:

How your brain hallucinates your conscious reality by Anil Seth:

How our brains use embodied simulation to construct meaning: (from Benjamin Bergen’s book Louder Than Words)


How even the structure of our vision is structured around predicting the immediate future from Mark Changizi:

This is another explanation of how vision is a constructed function (the rest of his talk covers similar ground to the Anil Seth talk):

Here’s a closer look at the image he is describing:

Here’s a good talk on Risk Literacy from Gerd Gigerenzer:

Emily Pronin et al found that people make different choices for their future selves, and that the decisions they make for their future selves are more like the decisions they might make for other people — we essentially have a “do as I say, not as I do” relationship with our future selves:

Similarly, seeing pictures of your aged self can impact your retirement planning:

Image of the scientist and his artificially aged self

While some of this is not immediately translatable into practical applications for learning and development, it does seem that construction of reality and future prediction is an important part of meaning-making and decision-making, which in turn impacts choices and behaviors.


Behavior Research Links

So, I was just talking to someone interesting in doing user research for behavior change, and I put together a set of links for her.  I thought it was a useful list, so also posting it here:

This is a nice collection of resources about UX User Research, including a list of people to follow:

A Habit-based Approach to Racial Bias

We all carry around implicit bias. It’s embedded in the culture, and it’s hideously obvious that it can lead to horrible tragic results.
This is study that has really been influencing my thinking about a habit-based approach to behavior change. The results actually show reduction in people’s implicit racial bias. It’s remarkable and rare to change something so deeply ingrained.
I’ve been using this study as an example of a habit-based approach to behavior change, but it seems timely to talk about these actual strategies — not as an example, but as an actual opportunity to improve our own bias. 
Here’s the actual study:

Long-term reduction in implicit race bias: A prejudice habit-breaking intervention
Patricia G. Devine, Patrick S. Forscher, Anthony J. Austin, and William T. L. Cox
J Exp Soc Psychol. 2012 Nov; 48(6): 1267–1278.

Here’s what they found:
Students took the Black-White Implicit Association Test (IAT) to test their level of implicit racial bias.  This test is adminstered via Harvard University. I recommend you try it yourself here:
Then participants engaged in five habit-based strategies to counteract their own implicit racial bias. This is important because participants watched for their own bias to show up and engaged in deliberately counteracting the incidents with one or more specific habit strategies. This gets at behavior rather than just intent.
Here are the specific strategies from the study:
  • Stereotype replacement
    This strategy involves replacing stereotypical responses for non-stereotypical responses. Using this strategy to address personal stereotyping involves recognizing that a response is based on stereotypes, labeling the response as stereotypical, and reflecting on why the response occurred. Next one considers how the biased response could be avoided in the future and replaces it with an unbiased response (Monteith, 1993). A parallel process can be applied to societal (e.g., media) stereotyping.
  • Counter-stereotypic imaging
    This strategy involves imagining in detail counter-stereotypic others (Blair et al., 2001). These others can be abstract (e.g., smart Black people), famous (e.g., Barack Obama), or non-famous (e.g., a personal friend). The strategy makes positive exemplars salient and accessible when challenging a stereotype’s validity.
  • Individuation
    This strategy relies on preventing stereotypic inferences by obtaining specific information about group members (Brewer, 1988; Fiske & Neuberg, 1990). Using this strategy helps people evaluate members of the target group based on personal, rather than group-based, attributes.
  • Perspective taking
    This strategy involves taking the perspective in the first person of a member of a stereotyped group. Perspective taking increases psychological closeness to the stigmatized group, which ameliorates automatic group-based evaluations (Galinsky & Moskowitz, 2000).
  • Increasing opportunities for contact
    This strategy involves seeking opportunities to encounter and engage in positive interactions with out-group members. Increased contact can ameliorate implicit bias through a wide variety of mechanisms, including altering the cognitive representations of the group or by directly improving evaluations of the group (Pettigrew, 1998; Pettigrew & Tropp, 2006).
The results were successful in reducing implicit racial bias (as measured by the IAT) for the intervention group:
As I mentioned above, this is an exceptional result.  Traditional diversity classes often produce good intentions but little behavior change, and rarely address the deep level of unconscious bias.
Hope this is helpful. – Julie

I’m an Elephant!

Specifically, I’m a Neon Elephant:


The Neon Elephant is an award from Dr. Will Thalheimer of Work-Learning Research, given for bridging the gap between research and learning practice.

This is really delightful, given the company of previous awardees:

  • 2014 – Peter C. Brown, Henry L. Roediger III, and Mark A. McDaniel for their book, Make it Stick: The Science of Successful Learning
  • 2013 –  Gary Klein
  • 2012 – K. Anders Ericsson
  • 2011 – Jeroen van Merriënboer
  • 2010 – Richard E. Clark
  • 2009 – Ruth Clark
  • 2008 – Robert Brinkerhoff
  • 2007 – Sharon Shrock and Bill Coscarelli
  • 2006  – Cal Wick

Lots of smart people on that list.  You should check our their stuff. Thanks Will!

(In other news, the second edition of the book is out.  I’ll be doing a separate announcement on that shortly).

The Best New Learning Book

The best new learning book doesn’t exactly look like a learning book, but trust me on this one, folks.

Cover of Badass: Making User Awesome, by Kathy Sierra


As I may have mentioned a few times in the past, Kathy Sierra’s stuff is FANTASTIC and this new book is no exception. I realize that nothing on the cover says “Learning & Development” exactly, but the mission of the title goes right to the heart of the whole purpose of L&D.

Specifically, though, this is one of the best accessible books out there that translates the science of expertise and skill development into compulsively readable material:




– images from Badass, used with permission

I read a review copy a few months ago, and have been stupid excited with anticipation of the book actually coming out. You can buy it here (and you should).



An Elearning Design Reading List


Several things have led to me actually writing a blog post.  First, I’m home for two whole weeks straight (this alone is a small miracle).  I’m also relatively up to date with my inbox and to do list (much larger miracles). I’m also indulging in some productive procrastination (which is probably the real reason).

Anyway, I typically keep a list of resources when I teach the ATD (ASTD) Advanced Instructional Design for Elearning Certificate, and I keep thinking that I should put the list somewhere.  So here it is:

Blogs et al:


Software Tools:

  • Branchtrack and Versal  – two interesting new elearning tools — can’t fully endorse them as they are still beta-ish, but interesting to look at.
  • Quandary Examples – a free (and unsupported) tool for making branched learning games.

Research-based Resources

Behavioral Economics


Anything by Kathy Sierra

The “I can’t believe I forgot…” Add-ons

Updated — some new books that have come out since I originally wrote this post:


Stephen Anderson – From Paths to Sandboxes

Sat in on Karl Fast and Stephen Anderson‘s Design for Understanding workshop at the IA Summit last week, and it was double-plus-good.

Here are Stephen’s slides from his IA Summit presentation.  Excellent stuff relating to autonomy in learning environments, and multitudes more:

Gameful Learning – More Sebastian Deterding Goodness

Okay, so I understand that it looks like I just post every Sebastian Deterding presentation on this blog, but really, I don’t.  He’s a prolific guy.  This one is specifically aimed at design for online learning, so it’s double-plus-good, and therefore must be posted here: